class BasicAgent(object):
"""
BasicAgent implements an agent that navigates the scene.
This agent respects traffic lights and other vehicles, but ignores stop signs.
It has several functions available to specify the route that the agent must follow,
as well as to change its parameters in case a different driving mode is desired.
"""
def __init__(self, vehicle, target_speed=20, opt_dict={}):
"""
Initialization the agent paramters, the local and the global planner.
:param vehicle: actor to apply to agent logic onto
:param target_speed: speed (in Km/h) at which the vehicle will move
:param opt_dict: dictionary in case some of its parameters want to be changed.
This also applies to parameters related to the LocalPlanner.
"""
self._vehicle = vehicle
self._world = self._vehicle.get_world()
self._map = self._world.get_map()
self._last_traffic_light = None
# Base parameters
self._ignore_traffic_lights = False
self._ignore_stop_signs = False
self._ignore_vehicles = False
self._target_speed = target_speed
self._sampling_resolution = 2.0
self._base_tlight_threshold = 5.0 # meters
self._base_vehicle_threshold = 5.0 # meters
self._max_brake = 0.5
# Change parameters according to the dictionary
opt_dict['target_speed'] = target_speed
if 'ignore_traffic_lights' in opt_dict:
self._ignore_traffic_lights = opt_dict['ignore_traffic_lights']
if 'ignore_stop_signs' in opt_dict:
self._ignore_stop_signs = opt_dict['ignore_stop_signs']
if 'ignore_vehicles' in opt_dict:
self._ignore_vehicles = opt_dict['ignore_vehicles']
if 'sampling_resolution' in opt_dict:
self._sampling_resolution = opt_dict['sampling_resolution']
if 'base_tlight_threshold' in opt_dict:
self._base_tlight_threshold = opt_dict['base_tlight_threshold']
if 'base_vehicle_threshold' in opt_dict:
self._base_vehicle_threshold = opt_dict['base_vehicle_threshold']
if 'max_brake' in opt_dict:
self._max_steering = opt_dict['max_brake']
# Initialize the planners
self._local_planner = LocalPlanner(self._vehicle, opt_dict=opt_dict)
self._global_planner = GlobalRoutePlanner(self._map,
self._sampling_resolution)
def add_emergency_stop(self, control):
"""
Overwrites the throttle a brake values of a control to perform an emergency stop.
The steering is kept the same to avoid going out of the lane when stopping during turns
:param speed (carl.VehicleControl): control to be modified
"""
control.throttle = 0.0
control.brake = self._max_brake
control.hand_brake = False
return control
def set_target_speed(self, speed):
"""
Changes the target speed of the agent
:param speed (float): target speed in Km/h
"""
self._local_planner.set_speed(speed)
def follow_speed_limits(self, value=True):
"""
If active, the agent will dynamically change the target speed according to the speed limits
:param value (bool): whether or not to activate this behavior
"""
self._local_planner.follow_speed_limits(value)
def get_local_planner(self):
"""Get method for protected member local planner"""
return self._local_planner
def get_global_planner(self):
"""Get method for protected member local planner"""
return self._global_planner
def set_destination(self, end_location, start_location=None):
"""
This method creates a list of waypoints between a starting and ending location,
based on the route returned by the global router, and adds it to the local planner.
If no starting location is passed, the vehicle local planner's target location is chosen,
which corresponds (by default), to a location about 5 meters in front of the vehicle.
:param end_location (carla.Location): final location of the route
:param start_location (carla.Location): starting location of the route
"""
if not start_location:
start_location = self._local_planner.target_waypoint.transform.location
clean_queue = True
else:
start_location = self._vehicle.get_location()
clean_queue = False
start_waypoint = self._map.get_waypoint(start_location)
end_waypoint = self._map.get_waypoint(end_location)
route_trace = self.trace_route(start_waypoint, end_waypoint)
self._local_planner.set_global_plan(route_trace,
clean_queue=clean_queue)
def set_global_plan(self,
plan,
stop_waypoint_creation=True,
clean_queue=True):
"""
Adds a specific plan to the agent.
:param plan: list of [carla.Waypoint, RoadOption] representing the route to be followed
:param stop_waypoint_creation: stops the automatic random creation of waypoints
:param clean_queue: resets the current agent's plan
"""
self._local_planner.set_global_plan(
plan,
stop_waypoint_creation=stop_waypoint_creation,
clean_queue=clean_queue)
def trace_route(self, start_waypoint, end_waypoint):
"""
Calculates the shortest route between a starting and ending waypoint.
:param start_waypoint (carla.Waypoint): initial waypoint
:param end_waypoint (carla.Waypoint): final waypoint
"""
start_location = start_waypoint.transform.location
end_location = end_waypoint.transform.location
return self._global_planner.trace_route(start_location, end_location)
def run_step(self):
"""Execute one step of navigation."""
hazard_detected = False
# Retrieve all relevant actors
actor_list = self._world.get_actors()
vehicle_list = actor_list.filter("*vehicle*")
lights_list = actor_list.filter("*traffic_light*")
vehicle_speed = get_speed(self._vehicle) / 3.6
# Check for possible vehicle obstacles
max_vehicle_distance = self._base_vehicle_threshold + vehicle_speed
affected_by_vehicle, _, _ = self._vehicle_obstacle_detected(
vehicle_list, max_vehicle_distance)
if affected_by_vehicle:
hazard_detected = True
# Check if the vehicle is affected by a red traffic light
max_tlight_distance = self._base_tlight_threshold + vehicle_speed
affected_by_tlight, _ = self._affected_by_traffic_light(
lights_list, max_tlight_distance)
if affected_by_tlight:
hazard_detected = True
control = self._local_planner.run_step()
if hazard_detected:
control = self.add_emergency_stop(control)
return control
def done(self):
"""Check whether the agent has reached its destination."""
return self._local_planner.done()
def ignore_traffic_lights(self, active=True):
"""(De)activates the checks for traffic lights"""
self._ignore_traffic_lights = active
def ignore_stop_signs(self, active=True):
"""(De)activates the checks for stop signs"""
self._ignore_stop_signs = active
def ignore_vehicles(self, active=True):
"""(De)activates the checks for stop signs"""
self._ignore_vehicles = active
def _affected_by_traffic_light(self, lights_list=None, max_distance=None):
"""
Method to check if there is a red light affecting the vehicle.
:param lights_list (list of carla.TrafficLight): list containing TrafficLight objects.
If None, all traffic lights in the scene are used
:param max_distance (float): max distance for traffic lights to be considered relevant.
If None, the base threshold value is used
"""
if self._ignore_traffic_lights:
return (False, None)
if not lights_list:
lights_list = self._world.get_actors().filter("*traffic_light*")
if not max_distance:
max_distance = self._base_tlight_threshold
if self._last_traffic_light:
if self._last_traffic_light.state != carla.TrafficLightState.Red:
self._last_traffic_light = None
else:
return (True, self._last_traffic_light)
ego_vehicle_location = self._vehicle.get_location()
ego_vehicle_waypoint = self._map.get_waypoint(ego_vehicle_location)
for traffic_light in lights_list:
object_location = get_trafficlight_trigger_location(traffic_light)
object_waypoint = self._map.get_waypoint(object_location)
if object_waypoint.road_id != ego_vehicle_waypoint.road_id:
continue
ve_dir = ego_vehicle_waypoint.transform.get_forward_vector()
wp_dir = object_waypoint.transform.get_forward_vector()
dot_ve_wp = ve_dir.x * wp_dir.x + ve_dir.y * wp_dir.y + ve_dir.z * wp_dir.z
if dot_ve_wp < 0:
continue
if traffic_light.state != carla.TrafficLightState.Red:
continue
if is_within_distance(object_waypoint.transform,
self._vehicle.get_transform(), max_distance,
[0, 90]):
self._last_traffic_light = traffic_light
return (True, traffic_light)
return (False, None)
def _vehicle_obstacle_detected(self,
vehicle_list=None,
max_distance=None,
up_angle_th=90,
low_angle_th=0,
lane_offset=0):
"""
Method to check if there is a vehicle in front of the agent blocking its path.
:param vehicle_list (list of carla.Vehicle): list contatining vehicle objects.
If None, all vehicle in the scene are used
:param max_distance: max freespace to check for obstacles.
If None, the base threshold value is used
"""
if self._ignore_vehicles:
return (False, None, -1)
if not vehicle_list:
vehicle_list = self._world.get_actors().filter("*vehicle*")
if not max_distance:
max_distance = self._base_vehicle_threshold
ego_transform = self._vehicle.get_transform()
ego_wpt = self._map.get_waypoint(self._vehicle.get_location())
# Get the right offset
if ego_wpt.lane_id < 0 and lane_offset != 0:
lane_offset *= -1
# Get the transform of the front of the ego
ego_forward_vector = ego_transform.get_forward_vector()
ego_extent = self._vehicle.bounding_box.extent.x
ego_front_transform = ego_transform
ego_front_transform.location += carla.Location(
x=ego_extent * ego_forward_vector.x,
y=ego_extent * ego_forward_vector.y,
)
for target_vehicle in vehicle_list:
target_transform = target_vehicle.get_transform()
target_wpt = self._map.get_waypoint(target_transform.location,
lane_type=carla.LaneType.Any)
# Simplified version for outside junctions
if not ego_wpt.is_junction or not target_wpt.is_junction:
if target_wpt.road_id != ego_wpt.road_id or target_wpt.lane_id != ego_wpt.lane_id + lane_offset:
next_wpt = self._local_planner.get_incoming_waypoint_and_direction(
steps=3)[0]
if not next_wpt:
continue
if target_wpt.road_id != next_wpt.road_id or target_wpt.lane_id != next_wpt.lane_id + lane_offset:
continue
target_forward_vector = target_transform.get_forward_vector()
target_extent = target_vehicle.bounding_box.extent.x
target_rear_transform = target_transform
target_rear_transform.location -= carla.Location(
x=target_extent * target_forward_vector.x,
y=target_extent * target_forward_vector.y,
)
if is_within_distance(target_rear_transform,
ego_front_transform, max_distance,
[low_angle_th, up_angle_th]):
return (True, target_vehicle,
compute_distance(target_transform.location,
ego_transform.location))
# Waypoints aren't reliable, check the proximity of the vehicle to the route
else:
route_bb = []
ego_location = ego_transform.location
extent_y = self._vehicle.bounding_box.extent.y
r_vec = ego_transform.get_right_vector()
p1 = ego_location + carla.Location(extent_y * r_vec.x,
extent_y * r_vec.y)
p2 = ego_location + carla.Location(-extent_y * r_vec.x,
-extent_y * r_vec.y)
route_bb.append([p1.x, p1.y, p1.z])
route_bb.append([p2.x, p2.y, p2.z])
for wp, _ in self._local_planner.get_plan():
if ego_location.distance(
wp.transform.location) > max_distance:
break
r_vec = wp.transform.get_right_vector()
p1 = wp.transform.location + carla.Location(
extent_y * r_vec.x, extent_y * r_vec.y)
p2 = wp.transform.location + carla.Location(
-extent_y * r_vec.x, -extent_y * r_vec.y)
route_bb.append([p1.x, p1.y, p1.z])
route_bb.append([p2.x, p2.y, p2.z])
if len(route_bb) < 3:
# 2 points don't create a polygon, nothing to check
return (False, None, -1)
ego_polygon = Polygon(route_bb)
# Compare the two polygons
for target_vehicle in vehicle_list:
target_extent = target_vehicle.bounding_box.extent.x
if target_vehicle.id == self._vehicle.id:
continue
if ego_location.distance(
target_vehicle.get_location()) > max_distance:
continue
target_bb = target_vehicle.bounding_box
target_vertices = target_bb.get_world_vertices(
target_vehicle.get_transform())
target_list = [[v.x, v.y, v.z] for v in target_vertices]
target_polygon = Polygon(target_list)
if ego_polygon.intersects(target_polygon):
return (True, target_vehicle,
compute_distance(target_vehicle.get_location(),
ego_location))
return (False, None, -1)
return (False, None, -1)
class PIDAgent(MapAgent):
def setup(self, path_to_conf_file):
super().setup(path_to_conf_file)
self.save_path = None
parent_folder = os.environ['SAVE_FOLDER']
string = pathlib.Path(os.environ['ROUTES']).stem
self.save_path = pathlib.Path(parent_folder) / string
def _init(self):
super()._init()
self._proximity_tlight_threshold = 5.0 # meters
self._proximity_vehicle_threshold = 10.0 # meters
self._local_planner = None
try:
self._map = self._world.get_map()
except RuntimeError as error:
print('RuntimeError: {}'.format(error))
print(' The server could not send the OpenDRIVE (.xodr) file:')
print(' Make sure it exists, has the same name of your town, and is correct.')
sys.exit(1)
self._last_traffic_light = None
self._proximity_tlight_threshold = 5.0 # meters
self._proximity_vehicle_threshold = 10.0 # meters
self._state = AgentState.NAVIGATING
# TBD: subject to change
self.target_speed = 7
args_lateral_dict = {
'K_P': 1,
'K_D': 0.4,
'K_I': 0,
'dt': 1.0/20.0}
self._local_planner = LocalPlanner(
self._vehicle, opt_dict={'target_speed' : self.target_speed,
'lateral_control_dict':args_lateral_dict})
self._hop_resolution = 2.0
self._path_seperation_hop = 2
self._path_seperation_threshold = 0.5
self._grp = None
self._map = CarlaDataProvider.get_map()
route = [(self._map.get_waypoint(x[0].location), x[1]) for x in self._global_plan_world_coord]
self._local_planner.set_global_plan(route)
def _get_control(self, tick_data, _draw):
pos = self._get_position(tick_data)
theta = tick_data['compass']
speed = tick_data['speed']
# is there an obstacle in front of us?
hazard_detected = False
# retrieve relevant elements for safe navigation, i.e.: traffic lights
# and other vehicles
actor_list = self._world.get_actors()
vehicle_list = actor_list.filter("*vehicle*")
lights_list = actor_list.filter("*traffic_light*")
# check possible obstacles
vehicle_state, vehicle = self._is_vehicle_hazard(vehicle_list)
if vehicle_state:
self._state = AgentState.BLOCKED_BY_VEHICLE
hazard_detected = True
# check for the state of the traffic lights
light_state, traffic_light = self._is_light_red(lights_list)
if light_state:
self._state = AgentState.BLOCKED_RED_LIGHT
hazard_detected = True
if hazard_detected:
control = self.emergency_stop()
else:
self._state = AgentState.NAVIGATING
# standard local planner behavior
control = self._local_planner.run_step(debug=DEBUG)
_draw.text((5, 90), 'Speed: %.3f' % speed)
_draw.text((5, 110), 'Target: %.3f' % self.target_speed)
return control
def run_step(self, input_data, timestamp):
if not self.initialized:
self._init()
data = self.tick(input_data)
rgb_with_car = cv2.cvtColor(input_data['rgb_with_car'][1][:, :, :3], cv2.COLOR_BGR2RGB)
data['rgb_with_car'] = rgb_with_car
topdown = data['topdown']
rgb = np.hstack((data['rgb_left'], data['rgb'], data['rgb_right']))
gps = self._get_position(data)
_topdown = Image.fromarray(COLOR[CONVERTER[topdown]])
_rgb = Image.fromarray(rgb)
_draw = ImageDraw.Draw(_topdown)
_topdown.thumbnail((256, 256))
_rgb = _rgb.resize((int(256 / _rgb.size[1] * _rgb.size[0]), 256))
_combined = Image.fromarray(np.hstack((_rgb, _topdown)))
_draw = ImageDraw.Draw(_combined)
# change
control = self._get_control(data, _draw)
steer = control.steer
throttle = control.throttle
brake = control.brake
_draw.text((5, 10), 'FPS: %.3f' % (self.step / (time.time() - self.wall_start)))
_draw.text((5, 30), 'Steer: %.3f' % steer)
_draw.text((5, 50), 'Throttle: %.3f' % throttle)
_draw.text((5, 70), 'Brake: %s' % brake)
if HAS_DISPLAY:
cv2.imshow('map', cv2.cvtColor(np.array(_combined), cv2.COLOR_BGR2RGB))
cv2.waitKey(1)
if self.step % 2 == 0:
self.gather_info()
record_every_n_steps = 3
if self.step % record_every_n_steps == 0:
self.save('', steer, throttle, brake, self.target_speed, data)
return control
def save(self, far_command, steer, throttle, brake, target_speed, tick_data):
# frame = self.step // 10
frame = self.step
speed = tick_data['speed']
string = os.environ['SAVE_FOLDER'] + '/' + pathlib.Path(os.environ['ROUTES']).stem
center_str = string + '/' + 'rgb' + '/' + ('%04d.png' % frame)
left_str = string + '/' + 'rgb_left' + '/' + ('%04d.png' % frame)
right_str = string + '/' + 'rgb_right' + '/' + ('%04d.png' % frame)
# topdown_str = string + '/' + 'topdown' + '/' + ('%04d.png' % frame)
center = self.save_path / 'rgb' / ('%04d.png' % frame)
left = self.save_path / 'rgb_left' / ('%04d.png' % frame)
right = self.save_path / 'rgb_right' / ('%04d.png' % frame)
topdown = self.save_path / 'topdown' / ('%04d.png' % frame)
rgb_with_car = self.save_path / 'rgb_with_car' / ('%04d.png' % frame)
data_row = ','.join([str(i) for i in [frame, far_command, speed, steer, throttle, brake, center_str, left_str, right_str]])
with (self.save_path / 'measurements.csv').open("a") as f_out:
f_out.write(data_row+'\n')
Image.fromarray(tick_data['rgb']).save(center)
Image.fromarray(tick_data['rgb_left']).save(left)
Image.fromarray(tick_data['rgb_right']).save(right)
# modification
Image.fromarray(COLOR[CONVERTER[tick_data['topdown']]]).save(topdown)
Image.fromarray(tick_data['rgb_with_car']).save(rgb_with_car)
def _draw_line(self, p, v, z, color=(255, 0, 0)):
if not DEBUG:
return
p1 = _location(p[0], p[1], z)
p2 = _location(p[0]+v[0], p[1]+v[1], z)
color = carla.Color(*color)
self._world.debug.draw_line(p1, p2, 0.25, color, 0.01)
def _is_light_red(self, lights_list):
"""
Method to check if there is a red light affecting us. This version of
the method is compatible with both European and US style traffic lights.
:param lights_list: list containing TrafficLight objects
:return: a tuple given by (bool_flag, traffic_light), where
- bool_flag is True if there is a traffic light in RED
affecting us and False otherwise
- traffic_light is the object itself or None if there is no
red traffic light affecting us
"""
ego_vehicle_location = self._vehicle.get_location()
ego_vehicle_waypoint = self._map.get_waypoint(ego_vehicle_location)
for traffic_light in lights_list:
object_location = self._get_trafficlight_trigger_location(traffic_light)
object_waypoint = self._map.get_waypoint(object_location)
if object_waypoint.road_id != ego_vehicle_waypoint.road_id:
continue
ve_dir = ego_vehicle_waypoint.transform.get_forward_vector()
wp_dir = object_waypoint.transform.get_forward_vector()
dot_ve_wp = ve_dir.x * wp_dir.x + ve_dir.y * wp_dir.y + ve_dir.z * wp_dir.z
if dot_ve_wp < 0:
continue
if is_within_distance_ahead(object_waypoint.transform,
self._vehicle.get_transform(),
self._proximity_tlight_threshold):
if traffic_light.state == carla.TrafficLightState.Red:
return (True, traffic_light)
return (False, None)
def _get_trafficlight_trigger_location(self, traffic_light): # pylint: disable=no-self-use
"""
Calculates the yaw of the waypoint that represents the trigger volume of the traffic light
"""
def rotate_point(point, radians):
"""
rotate a given point by a given angle
"""
rotated_x = math.cos(radians) * point.x - math.sin(radians) * point.y
rotated_y = math.sin(radians) * point.x - math.cos(radians) * point.y
return carla.Vector3D(rotated_x, rotated_y, point.z)
base_transform = traffic_light.get_transform()
base_rot = base_transform.rotation.yaw
area_loc = base_transform.transform(traffic_light.trigger_volume.location)
area_ext = traffic_light.trigger_volume.extent
point = rotate_point(carla.Vector3D(0, 0, area_ext.z), math.radians(base_rot))
point_location = area_loc + carla.Location(x=point.x, y=point.y)
return carla.Location(point_location.x, point_location.y, point_location.z)
def _bh_is_vehicle_hazard(self, ego_wpt, ego_loc, vehicle_list,
proximity_th, up_angle_th, low_angle_th=0, lane_offset=0):
"""
Check if a given vehicle is an obstacle in our way. To this end we take
into account the road and lane the target vehicle is on and run a
geometry test to check if the target vehicle is under a certain distance
in front of our ego vehicle. We also check the next waypoint, just to be
sure there's not a sudden road id change.
WARNING: This method is an approximation that could fail for very large
vehicles, which center is actually on a different lane but their
extension falls within the ego vehicle lane. Also, make sure to remove
the ego vehicle from the list. Lane offset is set to +1 for right lanes
and -1 for left lanes, but this has to be inverted if lane values are
negative.
:param ego_wpt: waypoint of ego-vehicle
:param ego_log: location of ego-vehicle
:param vehicle_list: list of potential obstacle to check
:param proximity_th: threshold for the agent to be alerted of
a possible collision
:param up_angle_th: upper threshold for angle
:param low_angle_th: lower threshold for angle
:param lane_offset: for right and left lane changes
:return: a tuple given by (bool_flag, vehicle, distance), where:
- bool_flag is True if there is a vehicle ahead blocking us
and False otherwise
- vehicle is the blocker object itself
- distance is the meters separating the two vehicles
"""
# Get the right offset
if ego_wpt.lane_id < 0 and lane_offset != 0:
lane_offset *= -1
for target_vehicle in vehicle_list:
target_vehicle_loc = target_vehicle.get_location()
# If the object is not in our next or current lane it's not an obstacle
target_wpt = self._map.get_waypoint(target_vehicle_loc)
if target_wpt.road_id != ego_wpt.road_id or \
target_wpt.lane_id != ego_wpt.lane_id + lane_offset:
next_wpt = self._local_planner.get_incoming_waypoint_and_direction(steps=5)[0]
if target_wpt.road_id != next_wpt.road_id or \
target_wpt.lane_id != next_wpt.lane_id + lane_offset:
continue
if is_within_distance(target_vehicle_loc, ego_loc,
self._vehicle.get_transform().rotation.yaw,
proximity_th, up_angle_th, low_angle_th):
return (True, target_vehicle, compute_distance(target_vehicle_loc, ego_loc))
return (False, None, -1)
def _is_vehicle_hazard(self, vehicle_list):
"""
:param vehicle_list: list of potential obstacle to check
:return: a tuple given by (bool_flag, vehicle), where
- bool_flag is True if there is a vehicle ahead blocking us
and False otherwise
- vehicle is the blocker object itself
"""
ego_vehicle_location = self._vehicle.get_location()
ego_vehicle_waypoint = self._map.get_waypoint(ego_vehicle_location)
for target_vehicle in vehicle_list:
# do not account for the ego vehicle
if target_vehicle.id == self._vehicle.id:
continue
# if the object is not in our lane it's not an obstacle
target_vehicle_waypoint = self._map.get_waypoint(target_vehicle.get_location())
if target_vehicle_waypoint.road_id != ego_vehicle_waypoint.road_id or \
target_vehicle_waypoint.lane_id != ego_vehicle_waypoint.lane_id:
continue
if is_within_distance_ahead(target_vehicle.get_transform(),
self._vehicle.get_transform(),
self._proximity_vehicle_threshold):
return (True, target_vehicle)
return (False, None)
@staticmethod
def emergency_stop():
"""
Send an emergency stop command to the vehicle
:return: control for braking
"""
control = carla.VehicleControl()
control.steer = 0.0
control.throttle = 0.0
control.brake = 1.0
control.hand_brake = False
return control